The amount of training data is really important for how well a model can work in different situations.
When there isn’t enough data, models can end up memorizing the examples instead of truly learning from them. This can cause big problems when they are faced with new, unseen data.
Here are some challenges that come with using too little data:
Limited diversity: Small sets of data might not show the real-life situations we want to prepare for.
Increased variance: The results can change a lot with only small differences in the data we use.
But don’t worry! There are some solutions:
Data augmentation: This means we can make our training sets bigger by creating new examples from the ones we already have.
Transfer learning: This is when we take a model that has already been trained on a big set of data and use it to help our model perform better.
By using these solutions, we can help our models learn better and do a great job in many different scenarios!
The amount of training data is really important for how well a model can work in different situations.
When there isn’t enough data, models can end up memorizing the examples instead of truly learning from them. This can cause big problems when they are faced with new, unseen data.
Here are some challenges that come with using too little data:
Limited diversity: Small sets of data might not show the real-life situations we want to prepare for.
Increased variance: The results can change a lot with only small differences in the data we use.
But don’t worry! There are some solutions:
Data augmentation: This means we can make our training sets bigger by creating new examples from the ones we already have.
Transfer learning: This is when we take a model that has already been trained on a big set of data and use it to help our model perform better.
By using these solutions, we can help our models learn better and do a great job in many different scenarios!